In Frontiers in digital health
Predictive models are increasingly being developed and implemented to improve patient care across a variety of clinical scenarios. While a body of literature exists on the development of models using existing data, less focus has been placed on practical operationalization of these models for deployment in real-time production environments. This case-study describes challenges and barriers identified and overcome in such an operationalization for a model aimed at predicting risk of outpatient falls after Emergency Department (ED) visits among older adults. Based on our experience, we provide general principles for translating an EHR-based predictive model from research and reporting environments into real-time operation.
Engstrom Collin J, Adelaine Sabrina, Liao Frank, Jacobsohn Gwen Costa, Patterson Brian W
2022
AI, EHR, falls prevention, machine learning, precision medicine, risk stratification
